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926–937 Nucleic Acids Research, 2017, Vol. 45, No. 2
doi: 10.1093/nar/gkw787
Published online 12 September 2016
In vivo single-RNA tracking shows that most tRNA
diffuses freely in live bacteria
Anne Plochowietz1 , Ian Farrell2,3 , Zeev Smilansky2 , Barry S. Cooperman3 and Achillefs
N. Kapanidis1,*
Biological Physics Research Group, Clarendon Laboratory, Department of Physics, University of Oxford, Parks
Road, OX1 3PU, Oxford, UK, 2 Anima Inc, 75 Claremont Road, Suite 102, Bernardsville, NJ 07924-2270, USA and
Department of Chemistry, University of Pennsylvania, 231 S. 34 Street, Philadelphia, PA 19104-6323, USA
Received September 4, 2015; Revised July 29, 2016; Accepted August 20, 2016
Transfer RNA (tRNA) links messenger RNA nucleotide sequence with amino acid sequence during
protein synthesis. Despite the importance of tRNA for
translation, its subcellular distribution and diffusion
properties in live cells are poorly understood. Here,
we provide the first direct report on tRNA diffusion
localization in live bacteria. We internalized tRNA labeled with organic fluorophores into live bacteria,
applied single-molecule fluorescence imaging with
single-particle tracking and localized and tracked
single tRNA molecules over seconds. We observed
two diffusive species: fast (with a diffusion coefficient of ∼8 ␮m2 /s, consistent with free tRNA) and
slow (consistent with tRNA bound to larger complexes). Our data indicate that a large fraction of internalized fluorescent tRNA (>70%) appears to diffuse freely in the bacterial cell. We also obtained
the subcellular distribution of fast and slow diffusing tRNA molecules in multiple cells by normalizing
for cell morphology. While fast diffusing tRNA is not
excluded from the bacterial nucleoid, slow diffusing
tRNA is localized to the cell periphery (showing a
30% enrichment versus a uniform distribution), similar to non-uniform localizations previously observed
for mRNA and ribosomes.
Studying the subcellular distribution of RNA in living cells
is vital for understanding the spatial organization of gene
expression (1), since this distribution can control the processes of transcription and translation and efficiently alter
protein activity. There are several examples of local RNA
enrichment in both eukaryotic and prokaryotic cells (2).
For instance, a preferential localization of ASH1 mRNA
encoding for an unstable transcriptional repressor protein
* To
in daughter cells has been shown in budding yeast (3), and
translation-independent subcellular localization of transcripts encoding for membrane and soluble proteins has
been observed in bacteria (4).
The subcellular RNA localization can be explored further through detection of individual RNA molecules and
measurement of their heterogeneity, a challenge that can be
met using single-molecule fluorescence microscopy. Initial
studies to visualize single mRNA molecules in cells used
fluorescence in situ hybridization (FISH) in fixed cells (5)
and mRNA tracking in live cells (6). Subsequent in vivo
studies of mRNA dynamics relied on indirect tagging of
mRNA with fluorescent proteins (FP) fused to the bacteriophage MS2 coat protein and its variants (PP7 system), or
to the human U1A protein (7), which directly binds to specific RNA hairpin sequences. However, FPs are less bright
and photostable than organic fluorophores (8,9) and require
more than 24 copies of MS2 binding sites (accommodating
48 GFPs) to localize single mRNA molecules (10), making
the MS2 array a bulky label (200 × 5 × 3 nm) that might
perturb mRNA interactions. An alternative approach used
live-cell tracking of mRNA molecules singly labeled using
organic fluorophores, e.g. by tagging the MS2 coat protein
via a polypeptide linker (SNAP tags (11)), or through RNA
aptamers (12,13); the latter method has also been used for
tracking single mRNA molecules in mammalian cells (14).
The spatial distribution of ribosomal RNA (rRNA) has
also been examined, with super-resolution imaging studies
in live bacteria showing exclusion of ribosomes from the cell
nucleoid (15); in contrast, ribosomal subunits S30 and S50
were shown to diffuse throughout the cell, and not to be
excluded from the nucleoid (16).
A third RNA species important for translation is transfer
RNA (tRNA), the complement of short and stable RNAs
(74–93 nt in length) that translate the nucleotide sequence in
an mRNA to the amino-acid sequence of the coded protein.
Despite the obvious importance of tRNA in translation, its
intracellular mobility and subcellular localization is essentially unknown. A major reason for this gap is the difficulty
whom correspondence should be addressed. Tel: +44 1865 272 401; Email: [email protected]
C The Author(s) 2016. Published by Oxford University Press on behalf of Nucleic Acids Research.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (, which
permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
Nucleic Acids Research, 2017, Vol. 45, No. 2 927
in labeling tRNA in vivo, since incorporation of RNA hairpins for MS2 or aptamer binding may interfere with tRNA
interaction with the aminoacyl synthetases, elongation factors or ribosomes. However, tRNA molecules can be labeled
in vitro by either derivatization of the charged amino acid
(17), or via covalent attachment of fluorophores to modified nucleosides with unique chemical reactivity (18–22).
The dihydrouridine (DHU) position in the D-loop of tRNA
can also be used to attach proflavine (23–26) or to introduce hydrazides for cyanine dye labeling (27). Such labeled
tRNA allowed many in vitro studies of translation dynamics using single-molecule fluorescence imaging (19,28) and
single-molecule FRET (21,29–31); tRNAs labeled with organic fluorophores have also been used to visualize protein
synthesis in both fixed and live mammalian cells through
FRET signals generated when labeled tRNAs bound at adjacent ribosomal sites (32,33).
Here, we studied the intracellular diffusion and subcellular distribution of tRNA using internalization of
fluorescent-labeled tRNA (fl-tRNA) into live Escherichia
coli through an electroporation-based method recently developed in our lab (34). Using single-particle tracking (35–
37), we traced individual tRNA molecules in live cells
and observed two modes of diffusion: a fast mode consistent with free tRNA, and a slow mode consistent with
tRNAs bound to ribosomes. tRNA molecules displayed a
peripheral distribution in cells, wherein fast diffusing tRNA
molecules were enriched at the nucleoid, and slow diffusing tRNA molecules localized predominantly at the cell periphery (similar to ribosomes). Antibiotic treatments and
controls demonstrated that the uneven tRNA distribution
patterns were translation-dependent. Our electroporation
method is general and paves the way for diverse singlemolecule fluorescence studies of a wide variety of RNA
molecules in live bacteria.
RNA labeling and purification
Total E. coli tRNA (Roche Diagnostics) were labeled at
DHU positions with Cy3, Cy5 or Cy5.5 as described in
(22,38). In all cases, the tRNA required HPLC purification as described in (32), in order to ensure removal
of free dye. After purification, labeled tRNA typically
contained 0.82 Cy3/tRNA, 0.80 Cy5/tRNA and 0.67
Cy5.5/tRNA. tRNAs were stored at −80◦ C in 10 and
1 ␮M stocks for use in single-cell and single-molecule
studies, respectively. Azide-modified 25-nt RNA (azideAAUUGUGAGAGCGGAUAACAAUUUC),
labeled with DBCO-Sulfo-Cy5 (gift by Alexandra Tomescu
and Tom Brown lab) and stored in 1 ␮M stocks at −20◦ C
for use in single-cell and single-molecules studies.
Electroporation and sample preparation
Commercially available electrocompetent E. coli cells, ElectroMAX DH5␣-E Competent Cells (Invitrogen), were used
for electroporation. Cells were diluted 1:1 with sterile milliQ water and stored at −80◦ C. For each electroporation experiment, 20 ␮l of electrocompetent cells were used.
Up to 2 ␮l of labeled tRNA stored in water milli-Q were
added to 20 ␮l electrocompetent cells (to final concentration
of 50 nM, 200 nM or 1 ␮M tRNA in electroporation cuvette). We also added 1 mM of ethylenediaminetetraacetic
acid (EDTA) to the mixture of electrocompetent cells and
labeled tRNA, which has to proven to stabilize nucleic acids
during electroporation (39). The cell suspension was transferred into a prechilled electroporation cuvette (0.1 cm gap
cuvette, Bio-Rad) and placed into an electroporator (MicroPulser, Bio-Rad). An electric field of 1.4 kV/cm was applied for electroporation and 500 ␮l of super optimal broth
with catabolite repression (SOC) was added immediately
after electroporation. Cells were recovered for 30 min at
37◦ C. After recovery, cells were harvested by centrifugation
at 3300 g for 1 min at 4◦ C and washed five-times with 500
␮l phosphate buffered saline (PBS). Cells were resuspended
in 80–120 ␮l PBS and placed on 1% agarose pads before
imaging. The agarose pads were made from about 500 ␮l of
M9 medium containing 1% (v:w) BioRad Certified Molecular Biology Agarose on a coverslip. About 8 ␮l of cells
were pipetted onto the pad, and a burned coverslip was
added on top. The slide/agar/slide sandwich was inverted
and placed on the microscope stage with the side containing the cells closest to the objective. For antibiotic-control
studies, 200 ␮g/ml chloramphenicol (Cam) or 200 ␮g/ml
rifampicin (Rif) was added to SOC medium and cells were
recovered for 30 min. Then, cells were washed as described
above except for the final dilution, where cells were diluted
in 100 ␮l PBS containing 200 ␮g/ml Cam or Rif and then
transferred to the agarose pad. Cells were fixed by incubating electroporated cells with 3% (v:v) paraformaldehyde for
1 h after recovery.
Live-cell imaging
Single-cell fluorescence microscopy in live bacteria was performed on a customized inverted Olympus IX-71 microscope equipped with two lasers, a 637-nm diode laser (Vortran Stradus, Vortran Laser Technology) and a 532-nm
DPSS laser (MGL-III-532-100mW, CNI). Laser light was
combined into a single-mode optical fiber (Thorlabs) and
collimated before focusing on the objective.
Cells were imaged using HILO illumination (40) by adjusting the position of the focused excitation light on the
back focal plane of the objective. Typical excitation powers
were 20–30 W/cm2 for single-cell fluorescence studies (internalization and counting studies) at 50 ms exposure time
using 532 and 637-nm laser lines. For single-molecule tracking studies, laser powers at 637 nm of 400 W/cm2 were used.
Stroboscopic illumination was employed for single-particle
tracking, exciting the sample for 1 ms while exposing the
camera chip for 5 ms to overcome motion blurring of fast
diffusing tRNA molecules. We note that for single-molecule
tracking studies, we sometimes pre-bleached entire fieldsof-view for up to 5 s before starting data acquisition to diminish the pool of fluorescent tRNA molecules such that
we were able to work on the single-molecule level and perform single-molecule localization and tracking analysis (see
Supplementary Data).
Cellular and single-molecule fluorescence was collected
through the same objective, filtered to remove excitation
928 Nucleic Acids Research, 2017, Vol. 45, No. 2
light through a long-pass filter (HQ545LP, Chroma) and
a notch filter (NF02-633S, Semrock), and spectrally separated by a dichroic mirror (630DRLP, Omega). Each
channel was imaged onto separate halves of an electronmultiplying charge-coupled device camera chip (iXon+, BI887, Andor). The illumination for bright-field images comprised a white-light lamp (IX2-ILL100, Olympus) and condenser (IX2-LWUCD, Olympus) attached to the microscope. Movies and images were recorded using the Andor
camera software.
Evaluation of cellular loading
Cells were categorized as loaded if their overall cellular fluorescence per cell area was larger than the mean plus three
standard deviation of the overall cellular fluorescence per
cell area of non-electroporated cells. Using this internalization threshold, <2% of the cells of non-electroporated
and non-treated control samples were classified as loaded.
More than 500 cells per sample were imaged and ∼85%
of cells were loaded when internalizing tRNA-Cy3, ∼98%
of cells with tRNA-Cy5, >60% of cells with tRNA-Cy5.5
and >85% of cells with ssRNA-Cy5. The lower loading efficiency of tRNA-Cy5.5 could be due to inefficient excitation of Cy5.5 on our setup (Cy5.5 at 637 nm: 40% of maximum absorption level, whereas Cy5 at 637 nm: 80% of maximum absorption level) and hence less fluorescence will be
detected above the cellular autofluorescence. The excitation
of the Cy5.5 cyanine dye could be further improved, but we
wanted to keep laser powers at 637-nm the same for Cy5
and Cy5.5 excitation for better comparison of their photobleaching lifetimes (Supplementary Figure S4).
Single-cell photobleaching studies and counting of tRNA
molecules per cell
We measured the fluorescence decay of entire field-of-views
containing 30–50 cells under continuous wave illumination
in the respective fluorescence channel. Cells were segmented
automatically by adapting the MATLAB implementation
‘Schnitzcells’ (41) for brightfield cell images. The manually adjusted cell masks were used to extract cells’ fluorescence data by calculating the total fluorescence intensity
per cell area within each cell mask for each movie frame.
Cell autofluorescence per cell area after photobleaching was
subtracted using a custom-written MATLAB script (Mathworks). This baseline subtraction was routinely performed
for all single-cell fluorescence time-traces but we note that
for photobleaching studies a single exponential fit (+offset) to the single-molecule fluorescence time-trace is sufficient to obtain the photobleaching life-time. Here, baselinesubtracted photobleaching time-traces of cells from singlecell studies (1 ␮M tRNA-Cy5 in electroporation cuvette,
Figure 2D) were fitted with a single exponential to obtain
the cell-based photobleaching lifetime as a measure of fluorophore photostability.
Baseline-subtracted photobleaching time-traces of cells
from single-molecule studies (∼50 nM of tRNA-Cy5 in
electroporation cuvette, Figure 2D), which showed <6
quantized steps, were fitted with a hidden Markov model
(HMM) as described in Ref. (34) to the unitary intensity.
Step heights recovered by the HMM algorithm from 158
photobleaching time traces per sample were plotted in the
step height histogram. The step-height histogram was fitted with a single one-dimensional-Gaussian (free fit parameters: position1, width1, amplitude1). The center of the fitted Gaussian peak corresponds to the fluorophore unitary
intensity, a measure of the fluorophore brightness. The unitary intensity of (8.7 ± 2.4) a.u. was obtained for Cy5, which
corresponds to (7500 ± 2100) photons/s. The number of
internalized tRNA molecules per cell was calculated by dividing the overall cellular fluorescence to the fluorophore
unitary intensity.
Single-molecule localization and single-particle tracking
Custom MATLAB (Mathworks) software was used to
analyze single-molecule tracking and diffusion in live E.
coli as described in (34) and (37). Briefly, the PSFs in each
movie frame were fitted by a 2D elliptical Gaussian (free fit
parameters: x/y position, x/y width, elliptical rotation angle, amplitude, background) using initial position guesses
from applying a fixed localization-intensity threshold on
the bandpass filtered fluorescence image (42)––software
available from our lab website (https://groups.physics.
Single-molecule tracking was performed by adapting the
MATLAB script based on a published algorithm (43).
Localized PSFs were linked to a trajectory if they appeared
in consecutive frames within a window of 7 pixels (∼0.67
␮m). This window size ensures 98% of steps are correctly
linked for a single diffusive species with an apparent
diffusion coefficient (Dapp ) of 4 ␮m2 /s and 5 ms exposure
time. To account for transient disappearance of the PSF
within a trajectory due to blinking or missed localization,
we used a memory parameter of one frame.
A detailed description of the single-particle tracking
analysis and the calculation of the cumulative distribution
function (CDF), apparent Dapp and fitting routines of the
CDF curves and Dapp -histograms is presented in detail in
the Supplementary Data. Since the apparent Dapp does not
take cellular confinement, motion blurring and localization
imprecision (37,44) into account, we simulated raw movies
of diffusing fluorescent molecules in a rod-shaped cylinder
to relate the apparent Dapp to an accurate Dapp ; further details are presented in the Supplementary Data.
tRNA spatial distribution analysis
Analysis was performed in MATLAB. Cells were segmented from bright-field images using MicrobeTracker
(45), obtaining a cell outline and cell midlines. The positions of the molecule trajectories were determined relative
to the cell midline, with the x-axis defined as the cell long
axis and the y-axis as the cell short axis. The distances were
normalized to 0 (cell midline) and 1 (cell outline). For the
probability distributions plots, localizations were normalized by their relative position along the short cell axis and
along the long cell axis (only absolute distance).
Nucleic Acids Research, 2017, Vol. 45, No. 2 929
The analytical probability distribution for a uniform distribution within a cylindrical volume is given by:
pd f (ρ) =
max r 2 − ρ 2 , 0
where ␳ is the radial coordinate and r is the cylinder radius,
for the unit cell holds r = 1.
Two-dimensional histograms of the spatial distribution
of tRNA molecules throughout the cell cycle were obtained
by binning cells according to cell length (short cells: 1.7–
2.9 ␮m and long cells: 3.1–4.3 ␮m) with short cells having
a single nucleoid located in the centre of the x-axis and and
long cells having two nucleoids (37).
Efficient internalization of organic dye-labeled RNAs into
live E. coli cells
We first tested whether short RNAs labeled with organic fluorophores could be internalized efficiently into live E. coli
using electroporation, as in previous studies showing efficient internalization of 45-bp ssDNA and dsDNA, as well
as proteins of up to 100 kDa (34,46–48). We reasoned that
RNAs shorter than ∼100 nt should be small enough to enter the cell during electroporation (34); such RNAs also
carry enough net negative charge to avoid any sticking to
the cell exterior (47). Additionally, we stabilized the RNA
by adding 1 mM EDTA to the electroporation buffer, since
this addition has proven to stabilize and internalize DNA
into live bacteria (39). In terms of fluorophores, we initially
selected Cy3, Cy5 and Cy5.5, due to their high photostability and brightness (46).
The RNA molecules delivered were uncharged bulk E.
coli tRNA (hydrodynamic radius, Rh = 2.6 nm, Supplementary Data), and a 25-mer ssRNA (‘RNA25’). Organic dyelabeled bulk E. coli tRNA molecules (Figure 1A), as well as
RNA25 were electroporated into electrocompetent E. coli
DH5␣ cells (‘Materials and Methods’ section) and imaged
under highly-inclined and laminated optical sheet illumination (40) in the respective fluorescent channel (Figure 1B).
Electroporation conditions were optimized (‘Materials and
Methods’ section) to maximize both RNA integrity and cell
viability, with ∼70% of cells growing and dividing after electroporation (46) (Supplementary Figure S1).
In all cases (bulk tRNA labeled with Cy3, Cy5, or Cy5.5
and RNA25-Cy5), RNA was efficiently internalized into
bacteria using electroporation (Figure 1C). For all samples,
electroporated cells showed higher intra-cellular fluorescence than non-electroporated cells or electroporated cells
without labeled RNA added (Figure 1C and Supplementary Figure S2). The internalization efficiency was calculated relative to non-electroporated cell control (to which labeled RNAs were added, not electroporated, washed and recovered like the electroporated sample), with >85% of cells
showing uptake of tRNA-Cy3, tRNA-Cy5 and RNA25Cy5 (Figure 1C, left to right) and >60% of cells showing
uptake of tRNA-Cy5.5 (Supplementary Figure S2).
Delivery of >100 labeled tRNA molecules into bacterial cells
We counted the number of internalized tRNA molecules per
cell using single-cell photobleaching (46). To obtain the unitary intensity corresponding to each internalized fluorescent tRNA, we first photobleached cells to the point where
only few (N < 10) fluorescent RNAs per cell remained, measured the decay of the overall cellular fluorescence in individual cells and obtained single-cell photobleaching timetraces (Figure 2A). The photobleaching time-trace shows
distinct single-step decreases of the overall cellular fluorescence, attributable to the photobleaching of a single fluorophore (blue single-cell intensity time-trace, Figure 2A)
and are used to relate the number of photobleaching steps
to the number of internalized tRNA-Cy5 molecules per cell.
We then fitted the photobleaching time-traces using HMM
(‘Materials and Methods’ section) and obtained the unitary
intensity for each photobleaching step (HMM fit in red,
gray bar highlights unitary intensity, Figure 2A). Single-cell
photobleaching time-traces from tRNA-Cy5 experiments
(50 nM tRNA-Cy5 in electroporation buffer) showed distinct step-like behavior (N = 158, Figure 2B), and HMM
analysis revealed the internalization of 1–3 molecules per
cell (Figure 2B and Supplementary Figure S3a). The unitary intensity for tRNA-Cy5 was 8.7 ± 2.4 a.u., corresponding to Cy5 brightness of ∼8000 photons/s (Figure 2C); the
tRNA-Cy3 and tRNA-Cy5.5 were less bright (Supplementary Figure S3b). The photon budget of a fluorophore is
important for accurate localization of a tRNA molecule,
since the localization precision is inversely proportional to
the square-root of the total photon count (49).
Next, we used single-cell photobleaching of cells loaded
with >100 molecules to measure the decay of fluorescence
over time and obtained photobleaching lifetimes for the different fluorophores (Supplementary Figure S4, Cy5: 3.4 ±
1.1 s, Cy3: 21.4 ± 5.8 s and Cy5.5: 4.6 ± 1.5 s). Since the
lifetimes were in the range of several seconds, and since a
tRNA spends only a fraction of a second in a ribosome
during translation (prokaryotic translation rates are 10–
20 aa/s (50,51)), the available observation time window
should allow the observation of tRNAs on a ribosome during polypeptide synthesis, something difficult for FP due to
their limited photostability (52).
We next used the measured unitary intensity to relate the
overall cellular fluorescence to the number of internalized
labeled tRNA molecules per cell. To determine the molecular density in cells that will allow us to localize single
tRNA molecules, we evaluated different initial concentrations of labeled tRNA-Cy5 before electroporation (i.e. in
the buffer surrounding the cells in the electroporation cuvette) by counting the number of tRNA molecules that were
eventually internalized per cell. We observed a wide distribution of internalized molecules per cell (Figure 2D), and
identified two working regimes: a ‘single-molecule regime’,
with 1–3 internalized labeled molecules/cell (50–250 nM
tRNA-Cy5: mean ∼1.5, median ∼1.0) and an ‘ensemble
regime’, with up to hundreds of internalized molecules per
cell (at 1 ␮M tRNA-Cy5: mean ∼140, median ∼100; see
also Supplementary Figure S5 for tRNA-Cy3 and tRNACy5.5).
930 Nucleic Acids Research, 2017, Vol. 45, No. 2
Figure 1. Internalization of labeled tRNA molecules into live Escherichia coli. (A) Schematic of labeled tRNA molecule. (B) Schematic of the internalization
method using electroporation (EP). Labeled tRNA molecules are added to cell suspension of electrocompetent cells and are exposed to the discharge of
a high-voltage electric field. Cells are quickly recovered and non-internalized tRNA molecules are washed off. Cell imaging is performed on an inverted
microscope using HiLO illumination. Scale bar: 1 ␮m. (C) Efficient loading of E. coli cells with tRNAs labeled with organic fluorophores of different
spectral range. Cells show high inner-cell fluorescence upon electroporation with 1 ␮M of tRNA-Cy3, tRNA-Cy5, and ssRNA-Cy5 (top row, left to right)
and basal fluorescence of non-electroporated cells corresponding to cellular autofluorescence in the respective spectral channel (‘−EP’ cells, bottom row).
More than 85% of cells were loaded when internalizing tRNA-Cy3, tRNA-Cy5 and ssRNA-Cy5 (>500 cells per dataset). Scale bar: 3 ␮m.
The diffusion profile of tRNA in live E. coli
We chose the single-molecule regime to study the localization and diffusive behavior of tRNA in the bacterial cytoplasm. We expected the diffusion of unbound tRNACy5 molecules to be in the 6–13 ␮m2 /s range, due to
the similarity in the hydrodynamic radii of tRNAs and
GFP (tRNA: 2.6–2.8 nm (53,54), GFP: 2.4 nm (55); Supplementary Data). To capture fast diffusion modes, we
used stroboscopic illumination (using 1-ms laser excitation pulses during 5-ms camera exposures) to obtain bright
diffraction-limited images of single tRNA molecules within
live bacterial cells (Figure 3A, t = 0 s). Each single tRNA
molecule was localized in each frame, and the localizations
within consecutive frames were linked to generate a singlemolecule trajectory (Figure 3A, yellow trajectory at t = 0.6,
0.9, 1.2 s, ‘Materials and Methods’ section).
The trajectories illustrated that tRNA molecules explore
most of the cellular environment over time (Figure 3A, Supplementary Figure S6a and Supplementary Movies 1 and
2). Using a large number of single-molecule trajectories
(N = 4122), we calculated the mean-squared displacement
(MSD) for tRNA molecules tracked in more than three consecutive frames. The MSD of tRNA-Cy5 in vivo (Figure 3B,
blue) plateau due to cellular confinement and localization
precision (44). We estimated the localization precision under these experimental conditions to 80 nm using a fixed-cell
control (Figure 3B, black).
We next calculated the distribution of apparent Dapp from
the MSDs of individual trajectories truncated to four steps,
(37,52) and Supplementary Data. The Dapp histograms for
tRNA-Cy5 show a bimodal distribution (Figure 3C and
Supplementary Figure S6e, g and h), and do not fit to an
analytical distribution of a single diffusive species (Figure
3C). We reasoned that tRNA molecules could be either
freely diffusing; or slowly diffusing when being bound to
moderate-size proteins (e.g. either to tRNA-synthetase or to
the elongation factor EF-Tu as a ternary complex of tRNA–
EF-Tu–GTP), or to the ribosomes during translation (19).
To explore these options, we fitted the Dapp -distribution
to two ␥ -distributions for two diffusive species, with the
slow diffusing species set to the Dapp expected for the assembled ribosome (Dapp = 0.5 ␮m2 /s; see ‘Discussion’ section) and the fast diffusing species left free; this fit (Figure
3C) recovered a major (∼90%) fast diffusing species with
Dapp ∼3.6 ␮m2 /s, which indicates considerable mobility for
tRNA. We also fitting the Dapp -distribution freely fitted to
one to three species (corresponding to ribosomal diffusion
and/or to ternary-complex diffusion) and still obtained a
major (∼70%) mobile population (Supplementary Figure
S6d–f). The apparent Dapp from the free fits of the Dapp distribution to two or three species ranged from Dapp = 3.3
± 1.6 ␮m2 /s (free, two species), Dapp = 3.5 ± 1.7 ␮m2 /s
(free, three species) and shows that the major tRNA species
was fast diffusing similar to the apparent Dapp obtained
from constrained fits of the slow diffusing species to ribosomal and ternary complex diffusion Dapp = 3.4 ± 1.7 ␮m2 /s
(constrained, two species), Dapp = 3.7 ± 1.8 ␮m2 /s (constrained, three species), Supplementary Figure S6g–i. We
estimated the Dapp of the ternary complex to be ∼2.4 ␮m2 /s
under our experimental conditions using diffusion simulations (Supplementary Figure S7a), since the accurate Dapp
of the protein Fis (with similar size of the ternary complex
(56)) was determined to D∼4.5 ␮m2 /s (37). Including more
data sets for bulk tRNA and testing several fitting modes
(single species, constrained to fixed cell control, constrained
to ternary complex and ribosomal diffusion, Supplementary Figure S6g–i) we obtained a Dapp (tRNA-Cy5) = 3.5
± 0.2 ␮m2 /s for the mobile species. We note that data sets
Nucleic Acids Research, 2017, Vol. 45, No. 2 931
Figure 2. Counting and controlling the number of internalized tRNA molecules per cell. (A) Single-cell photobleaching step analysis of tRNA-Cy5 in live
bacteria. Scale bar: 1 ␮m. (B) Photobleaching time-trace samples (blue) and Hidden–Markov Model fit (red) of single photobleaching steps corresponding
to the single-molecule unitary intensity (gray bars). (C) Step height histogram (unitary intensity distribution) of tRNA-Cy5 (158 photobleaching timetraces, 324 fitted single photobleaching steps = unitary intensity) and Gaussian fit centered at (8.7 ± 2.4) a.u. corresponding to Cy5 brightness of (7500 ±
2100) photons/s. (D) Counting the number of internalized tRNA-Cy5 molecules per cell at different concentrations of labeled tRNA-Cy5 in cell suspension
before electroporation. Cells are loaded with 1–10 tRNA molecules when adding 50 nM tRNA-Cy5 (mean: 1.4 molecules per cell, median: 0.9) and up to
250 nM tRNA-Cy5 (mean: 1.5 molecules per cell, median: 1.0) in the cell suspension before electroporation, which is an ideal regime for single-molecule
studies. Cells are loaded with about 100 tRNA molecules per cell (mean: ∼140, median: ∼100, Supplementary Figure S5 for tRNA-Cy3 and tRNA-Cy5.5)
at 1 ␮M tRNA-Cy5 initial concentration before electroporation.
taken on different days show different proportion of slow
diffusing molecules (5–11%), which could be due to variability of the electroporation process and cellular uptake. However, even when fitting the Dapp distribution to two or three
diffusive species, we always obtained a major fast diffusing
species (>70%) that matched the expected value for freely
diffusing tRNA molecules (Figure 3C and Supplementary
Figure S6d–i).
Since the apparent Dapp is affected by experimental conditions (cellular confinement, motion blurring, localization
precision), we simulated single-molecule tracking movies to
relate the measured Dapp to accurate Dapp in vivo. We simulated free Brownian motion of single tRNA molecules in
a confined cellular environment mimicking similar experimental conditions and performed the same single-molecule
tracking analysis as for experimental data (Supplementary
Data; ∼1.5 molecules per cell, 500 cells for each simulation
run, Supplementary Movie 3 and 4). This calibration of the
measured Dapp with the actual Dapp input for the simulations (Supplementary Figure S7a and b) resulted in an accurate Dapp of DtRNA = 8.1(−1.0 + 0.5) ␮m2 /s (Supplementary Figure S7c and d).
Non-uniform spatial distribution of tRNA molecules in vivo
To determine the tRNA spatial distribution, we plotted the
localizations of fast diffusing tRNA molecules (blue) and
slow diffusing tRNA molecules (red) within a unit cell, normalizing along cell-length and cell-width (Figure 4Ai). We
used a threshold of D < 1 ␮m2 /s to select slow diffusing
molecules, such that 90% of all trajectories in the fixed-cell
932 Nucleic Acids Research, 2017, Vol. 45, No. 2
Figure 3. Single-molecule tracking of tRNA-Cy5 and measurement of diffusion coefficient (Dapp ) of tRNA in vivo. (A) Tracking of a single tRNA-Cy5
molecule for ∼1.5 s and reconstruction of the single-molecule trajectory in the bacterial cell (90 FOV, 700 cells and ∼20 000 localizations for live-cell dataset
and ∼200 localizations for negative controls −tRNA + EP and +tRNA − EP). Scale bar: 1 ␮m. (B) Measurement of the mean-squared displacement
(MSD) for tRNA-Cy5 in live cells (blue) and fixed cells (black). The MSD plateaus due to cellular confinement and motion blurring in live cells and due
to the localization precision. We calculated the localization precision ␴ from the apparent Dapp of the fixed cell control Dapp (fixed cells) = 0.32 ␮m2 /s =
␴ 2 /(4t), with t = 5ms and obtained ␴ = 0.08 ␮m, Supplementary Figure S6b. (C) Apparent Dapp distribution of tRNA-Cy5 and fit to two diffusive
species. The slow diffusive species was constrained to ribosomal complex diffusion of Dapp (ribosome) = 0.5 ␮m2 /s (red, 5%, Ref (26)) and free fit of fast
diffusing species resulted in Dapp (tRNA-Cy5) = 3.6 ␮m2 /s (blue, 95%). Similar fitting results were obtained when constraining the slow diffusing species
to immobile molecules of the fixed-cell control, Dapp (fixed cells) = 0.32 ␮m2 /s (Supplementary Figure S6b, c and g) or when allowing a free fit to one,
two and three diffusive species (Supplementary Figure S6d–f). Including another independent dataset (90 FOV, 582 cells), the apparent Dapp of tRNA in
live cells was obtained to Dapp (tRNA-Cy5) = (3.5 ± 0.2) ␮m2 /s (Supplementary Figure S6h). (D) Cumulative distribution function (CDF) of tRNA-Cy5
tracking data was freely fitted to two diffusive species and the Dapp of the fast diffusing species was obtained to D2 = 7.6 ␮m2 /s (slow diffusive species: D1
= 0.12 ␮m2 /s).
control were categorized as slow diffusing (Supplementary
Figure S6b and c).
The localization maps show a distinct enrichment of slow
diffusing tRNA molecules at the cell periphery (defined as
the intracellular space between the nucleoid and the inner
membrane), whereas fast diffusing molecules localized with
a slight preference for proximity to the mid-cell region, a
region occupied by the bacterial nucleoid. This was also evident after plotting the probability density of slow and fast
diffusing molecules along the short axis of the unit cell (Figure 4Aii, top: unit cell showing cell periphery (gray) and
mid-cell (beige)); slow diffusing tRNA molecules were enriched at the cell periphery, and were partly excluded from
the mid-cell region (Figure 4Aii, slow diffusing species (red)
relative to uniform distribution (dotted line): −24% at midcell and +33% at cell periphery). Furthermore, the fast diffusing localized predominantly along mid-cell (Figure 4Aii,
fast diffusing species (blue) relative to uniform distribution
(dotted line): +10% at mid-cell and −19% at cell periphery),
as previously observed for the nucleoid-associated protein
HU (16,52).
To dissect further the exclusion of slow diffusing tRNA
molecules from the mid-cell region and the nucleoid, we
sorted cells along cell length (cell length distributions in
Supplementary Figure S8). Specifically, we looked at short
cells (1.7–2.9 ␮m long), which are expected to have only
a single chromosome) and long cells (3.1–4.3 ␮m long),
which contain a chromosome that has been largely duplicated (dataset 1 and Supplementary Figure S10). For short
cells, we again observed a clear tendency of slow diffusing
molecules to localize at the cell periphery (especially close
to the cell endcaps), while fast diffusing molecules explore
predominantly the mid-cell region (Supplementary Figure
The spatial distribution of fast diffusing molecules within
long cells was similar to that of short cells, apart from
the appearance of two local maxima along the long cellmidline; this latter feature suggests the presence of two chromosomes, from which slow diffusing tRNA molecules were
largely absent, again enriching the cell periphery (Supplementary Figure S10). This further indicates that mobile
tRNA molecules could diffuse freely and explore the en-
Nucleic Acids Research, 2017, Vol. 45, No. 2 933
Figure 4. Spatial distribution of tRNA molecules in live bacterial cells. Localizations of tRNA-Cy5 molecules were normalized along cell-length and cellwidth and were represented in unit cell (‘Materials and Methods’ section). (Ai) Localizations of tRNA molecules classified as fast diffusing (Dapp > 1
␮m2 /s, blue) and slow diffusing (red) and combined (top) were shown in unit cells, respectively. (Aii) Probability distribution of fast and slow diffusing
molecules along cell width (y-axis, short cell axis). Bias of slow diffusing molecules to cell periphery and fast diffusing molecules to mid-cell relative to
uniform-distribution (dotted line). (B) Spatial distribution of tRNA molecules upon Chloramphenicol treatment blocking translation. (Bi) Representation
as in Ai. (Bii) Fast and slow diffusing tRNA molecules were evenly distributed along cell widths. Results in (A and B) indicate that slow diffusing tRNA
molecules at the cell periphery were utilized during translation. The spatial distribution of control measurements such as unspecific RNA, and fixed cells
are shown along cell within Supplementary Figure S9 and within a unit cell in Supplementary Figure S10 for different cell length.
tire nucleoid, whereas slow diffusing tRNA molecules localize at the cell periphery and are not binding significantly
within the nucleoid interior. Notably, the 12% fraction of
slow-to-fast diffusing tRNA molecules (using a simple Dapp
threshold) does not change throughout the cell cycle (since
we observe 13 and 11% for this fraction in short and long
cells, respectively) and is in good agreement with the result
from our Dapp -distribution fit (Figure 3C).
Blocking translation changes the tRNA localization for slow
diffusing molecules
The peripheral localization pattern of slow diffusing tRNA
throughout the cell cycle is consistent with the internalized tRNA being utilized in protein synthesis, since previous
studies showed the exclusion of ribosomal complexes from
the nucleoid (16). To test this hypothesis, we treated cells
with the antibiotic chloramphenicol (Cam, which binds to
the 50S ribosomal subunit, preventing tRNA accommodation at the A-site and inhibiting peptide bond forma-
tion (57)), and repeated the analysis (Figure 4Bi). Indeed,
chloramphenicol treatment resulted in a much more uniform distribution for the slow diffusing tRNA, with loss
of the cell-periphery bias (with the fraction of molecule in
the periphery decreasing from +33 to +14%) (Figure 4Bii);
this loss was seen for all cell lengths (Supplementary Figure S10). The new picture after chloramphenicol treatment
shows that both slow and fast diffusing tRNA molecules
localize evenly throughout the entire cell. These results suggest that the peripheral tRNA localization pattern for slow
diffusing tRNA was due to their binding to ribosomes with
peripheral localization (see ‘Discussion’ section).
Further evidence of translation-dependent localization of
slow diffusing tRNA molecules at the cell periphery was
obtained by studying the spatial distribution of tRNA-Cy5
molecules in fixed cells; non-specific ssRNA-Cy5 (RNA25)
molecules in vivo; and through Monte-Carlo diffusion simulations (Supplementary Figure S9). In all three controls, the
internalized molecules were uniformly distributed within
the cell and no peripheral localization pattern was observed.
934 Nucleic Acids Research, 2017, Vol. 45, No. 2
Detection and localization of small RNAs in live bacteria
Using an electroporation-based technique, we efficiently internalized fluorescent RNA molecules into live bacteria.
The internalization of RNA labeled with bright and photostable organic-dyes opens new ways for studying many
forms of stable RNAs (tRNAs, rRNA fragments) and regulatory RNAs (riboswitches and 5S RNA). Labeling with
small organic fluorophores is expected to be much less perturbing than the introduction of ∼1000-fold larger probes
based on FP. The use of fluorophores such as Cy3, Cy5 or
Cy5.5 opens a wide spectral range (500 to 700 nm) for in
vivo fluorescence studies. This range has the benefit of lower
cellular autofluorescence, especially in the red and infrared
spectrum (58,59). Apart from the novel single-molecule sensitivity (tracking of individual tRNAs) described here, the
internalization of molecules at the small-ensemble regime
(10–100 molecules per cell) should enable other fluorescence imaging techniques, such as fluorescent recovery after
photobleaching (FRAP) and fluorescence correlation spectroscopy (FCS) studies.
Single-particle tracking shows that tRNA exhibits mainly
free diffusion in E. coli
Despite its obvious importance, the diffusion properties of
tRNA in vivo were poorly characterized in all organisms, including bacteria. Early measurements in cell lysates yielded
an estimate of D of ∼60 ␮m2 /s in vitro (60), whereas diffusion simulations using a bead-model of tRNA estimated a
much lower value (D∼10 ␮m2 /s; (61)). Due to the similar
size of tRNA molecules and GFP (hydrodynamic radius of
2.6 and 2.4 nm for yeast tRNAPhe (53,54) and GFP (55),
respectively), we expected that, if tRNA diffuses freely in
vivo, its Dapp will be only ∼10% smaller than for GFP. The
GFP Dapp was in the range of 6–10 ␮m2 /s using FCS and
FRAP (62); however, single-molecule tracking studies of
FP such as mCherry, Venus, Dendra2 and mEos2 obtained
slightly higher Dapp (7–13 ␮m2 /s) in the E. coli cytoplasm
((16,36,52,63), Supplementary Data). Hence, the Dapp of 8.1
± 1.0 ␮m2 /s for tRNA (Figure 3C and D, Supplementary
Figure S6d–h and simulations Supplementary Figure S7) is
in excellent agreement with the values expected from free
Based on the high equilibrium association constant (7 ×
106 M−1 ; (64)) of aa-tRNA binding to EF-Tu-GTP (ternary
complex), and the observation in eukaryotic cells of ‘tRNA
channeling’ (a process where tRNA is directly transferred
from tRNA synthetases to elongation factors and then to
ribosomes without dissociating; (65)), we tested the prediction of such a mechanism in E.coli by fitting the apparent
Dapp distribution with two constrained species (ribosomal
diffusion and ternary-complex diffusion). However, we still
obtained about 70% of a fast diffusing species (with Dapp >
3.5 ␮m2 /s).
Our results show that a large fraction of internalized fluorescent tRNA (>70%) appears to diffuse freely in the bacterial cell. This result is surprising, since it has often been assumed (but has never been shown) that most tRNA is found
in a bound state in the cell, either as a complex with tRNA
synthetase, or a ternary complex with elongation factor EFTu and GTP, or engaged in the ribosome during translation.
We considered possible trivial reasons for the high mobility of internalized fluorescent tRNA. One possibility is
that the labeled tRNA may have reduced functionality;
however, in vitro studies showed that the labeled tRNA is
charged normally with amino acids, and that charged-andlabeled tRNA efficiently binds to elongation factor EF-Tu
(22). A second possibility is that the binding of electroporated tRNA to tRNA-synthetase is prevented by preferential binding of endogenous tRNA by tRNA-synthetases (as
part of a bacterial tRNA channeling process), as shown
for electroporated tRNAs in eukaryotic cells (65). However,
there is no evidence for any tRNA channeling in bacteria; in
fact, bacterial tRNA-synthetases (66) lack tRNA-binding
domains, which are present in most eukaryotic tRNAsynthetases and dictate their much more stable tRNA binding. A third possibility is that the electroporation process
has changed cell physiology to a degree that it led to tRNA
dissociation from its protein or ribosomal partners; since
70% of the cells grow and divide normally after electroporation, deposition and imaging, we consider that this possibility is unlikely to be the main factor for the large fraction
of freely diffusing tRNA we observe.
Another consideration is that many of our expectations
regarding interaction equilibria in vivo come from either in
vitro studies or indirect cellular studies. For example, although EF-Tu may be abundant in E. coli, its potential interactions with actin-like protein MreB (shown in Bacillus
subtilis and E. coli (67,68)) will decrease significantly the
amount of free EF-Tu in cells. Further, the in vivo affinity
of tRNA for its cellular interaction partners (tRNA synthetase, EF-Tu-GTP) is currently unknown and may be
weaker than measured in vitro.
Given these considerations, our results strongly suggest
that most tRNA molecules diffuse freely in cells, supporting
a simple mechanism for locating tRNA interaction partners
within bacteria; such as a ‘diffusion-based tRNA’ cycle in
bacteria (due to the lack of subcellular structure and higher
tRNA concentration in prokaryotes compared to eukaryotes) has already been proposed (66).
We also examined whether a diffusion-based tRNA cycle (where tRNAs spend most of their time searching for
ribosome using free diffusion and participating in peptidyl
transfer reaction in the ribosome) is fast enough to sustain
the speed of protein synthesis. Assuming translation rates
of 50 ms per amino acid (50) with two tRNA molecules
(endogenous tRNA pool: 375 000) present at all 50 000 ribosomes per E. coli cell (69,70), we expect 100 000 tRNA
molecules bound to ribosomes and 275 000 molecules
searching for ribosome sites, resulting to a ribosome-bound
tRNA fraction of ∼25%. The fraction also reflects the fraction of tRNA cycle time dedicated to ribosome binding;
since the ribosome binding time for a tRNA is 2 × 50
ms = 100 ms, the total cycle is ∼400 ms and the searchand-charging time is ∼300 ms. Indeed, a tRNA molecule
can explore the entire cell within 70 ms assuming threedimensional free diffusion with DtRNA = 8 ␮m2 /s and E.
coli cell dimensions of 3 × 1 ␮m: MSD(t) = 6DtRNA t ≥ 3
␮m2 → t ≥ 70 ms, which is ∼25% of the time the tRNA is
allowed to recharge and search for the ribosome assuming a
Nucleic Acids Research, 2017, Vol. 45, No. 2 935
diffusion-based tRNA cycle and rapid cell growth. This calculation shows that the tRNA diffusion is not rate-limiting
for protein synthesis in bacteria.
Peripheral distribution of slow diffusing tRNA molecules
We observed substantial enrichment of slow-diffusing
tRNA at the periphery compared to uniform distribution;
this enrichment was more apparent when cells were sorted
according to size. We propose that tRNA molecules classified as ‘slow diffusing’ associate with ribosomes, since the
slow diffusing species matched an apparent Dapp distribution of 0.3–0.5 ␮m2 /s (Supplementary Figure S7), consistent with ribosomal diffusion (36); further, slow diffusing
tRNA molecules were biased toward the cell periphery, consistent with the observed exclusion of ribosomes from the
cell nucleoid (15,71). Alternative interpretations of the data
include tRNA complexation with other large macromolecular assemblies.
Our results also show that the fast diffusing tRNA
molecules diffuse freely within the entire nucleoid. Along
with studies showing that ribosomal subunits S30 and S50
can penetrate the entire cell and are not excluded from the
nucleoid (16), our tRNA diffusion and localization results
provide further support that heavily transcribed and translated genes move to the nucleoid periphery, as suggested for
co-transcriptional translation.
The ∼10% slow-diffusing fraction of tRNA is within
the expected values for bacteria with a division time of
∼30 min. Considering that ∼2 internalized labeled tRNACy5 molecules per cell compete with the intrinsic pool of
∼375 000 unlabeled tRNA for maximum two binding sites
at ∼50 000 ribosomes per cell (69,70), we estimate an upper limit of 25% of slow diffusing tRNA molecules at the
ribosome (based on slow diffusing tRNA = number of ribosomal sites/total amount of tRNA).
Following blocking translation with chloramphenicol
(which also has indirect effects due a decrease in the rate
of ribosome biogenesis (72)), the periphery bias of slow diffusing tRNAs is drastically reduced (+14% at the cell periphery with Cam versus +33% without Cam), supporting
our proposal that tRNA molecules are slowly diffusing with
ribosomal complexes outside the nucleoid (15,16).
Supplementary Data are available at NAR Online.
We thank Tom Brown, Afaf El-Sagheer and Alexandra
Tomescu for the ssRNA fragment, Mathew Stracy and
Jaroslaw Nowak for software, Iris Alroy, David Bauer for
comments on the manuscript and Pawel Zawadzki for useful discussions.
UK Engineering and Physical Sciences Research Council (EPSRC) (to A.P.); German National Academic Foundation (Studienstiftung); Phizackerley Senior Scholarship
in Medical Sciences at Balliol College, Oxford (to A.P.);
UK BBSRC Grant [BB/J001694/1 to A.N.K.]; Wellcome
Trust Investigatorship [110164/Z/15/Z]; European Research Council Starter [ERC 261227]. Funding for open access charge: Wellcome Trust (to A.N.K.).
Conflict of interest statement. None declared.
1. Buxbaum,A.R., Haimovich,G. and Singer,R.H. (2015) In the right
place at the right time: visualizing and understanding mRNA
localization. Nat. Rev. Mol. Cell Biol., 16, 95–109.
2. Eliscovich,C., Buxbaum,A.R., Katz,Z.B. and Singer,R.H. (2013)
mRNA on the move: the road to its biological destiny. J. Biol. Chem.,
288, 20361–20368.
3. Long,R.M., Singer,R.H., Meng,X., Gonzalez,I., Nasmyth,K. and
Jansen,R.P. (1997) Mating type switching in yeast controlled by
asymmetric localization of ASH1 mRNA. Science, 277, 383–387.
4. Nevo-Dinur,K., Nussbaum-Shochat,A., Ben-Yehuda,S. and
Amster-Choder,O. (2011) Translation-independent localization of
mRNA in E. coli. Science, 331, 1081–1084.
5. Femino,A.M., Fay,F.S., Fogarty,K. and Singer,R.H. (1998)
Visualization of single RNA transcripts in situ. Science, 280, 585–590.
6. Pitchiaya,S., Heinicke,L.A., Custer,T.C. and Walter,N.G. (2014)
Single molecule fluorescence approaches shed light on intracellular
RNAs. Chem. Rev., 114, 3224–3265.
7. Brodsky,A.S. and Silver,P.A. (2002) Identifying proteins that affect
mRNA localization in living cells. Methods, 26, 151–155.
8. Shaner,N.C., Steinbach,P.A. and Tsien,R.Y. (2005) A guide to
choosing fluorescent proteins. Nat. Methods, 2, 905–909.
9. Dempsey,G.T., Vaughan,J.C., Chen,K.H., Bates,M. and Zhuang,X.
(2011) Evaluation of fluorophores for optimal performance in
localization-based super-resolution imaging. Nat. Methods, 8,
10. Fusco,D., Accornero,N., Lavoie,B., Shenoy,S.M., Blanchard,J.M.,
Singer,R.H. and Bertrand,E. (2003) Single mRNA molecules
demonstrate probabilistic movement in living mammalian cells. Curr.
Biol., 13, 161–167.
11. Carrocci,T.J. and Hoskins,A.A. (2014) Imaging of RNAs in live cells
with spectrally diverse small molecule fluorophores. Analyst, 139,
12. Dolgosheina,E.V., Jeng,S.C., Panchapakesan,S.S., Cojocaru,R.,
Chen,P.S., Wilson,P.D., Hawkins,N., Wiggins,P.A. and Unrau,P.J.
(2014) RNA mango aptamer-fluorophore: a bright, high-affinity
complex for RNA labeling and tracking. ACS Chem. Biol., 9,
13. Paige,J.S., Wu,K.Y. and Jaffrey,S.R. (2011) RNA mimics of green
fluorescent protein. Science, 333, 642–646.
14. Zimyanin,V.L., Belaya,K., Pecreaux,J., Gilchrist,M.J., Clark,A.,
Davis,I. and St Johnston,D. (2008) In vivo imaging of oskar mRNA
transport reveals the mechanism of posterior localization. Cell, 134,
15. Bakshi,S., Siryaporn,A., Goulian,M. and Weisshaar,J.C. (2012)
Superresolution imaging of ribosomes and RNA polymerase in live
Escherichia coli cells. Mol. Microbiol., 85, 21–38.
16. Sanamrad,A., Persson,F., Lundius,E.G., Fange,D., Gynna,A.H. and
Elf,J. (2014) Single-particle tracking reveals that free ribosomal
subunits are not excluded from the Escherichia coli nucleoid. Proc.
Natl. Acad. Sci. U.S.A., 111, 11413–11418.
17. McIntosh,B., Ramachandiran,V., Kramer,G. and Hardesty,B. (2000)
Initiation of protein synthesis with fluorophore-Met-tRNA(f) and the
involvement of IF-2. Biochimie, 82, 167–174.
18. Schleich,H.G., Wintermeyer,W. and Zachau,H.G. (1978)
Replacement of wybutine by hydrazines and its effect on the active
conformation of yeast tRNAPhe. Nucleic Acids Res., 5, 1701–1713.
19. Blanchard,S.C., Gonzalez,R.L., Kim,H.D., Chu,S. and Puglisi,J.D.
(2004) tRNA selection and kinetic proofreading in translation. Nat.
Struct. Mol. Biol., 11, 1008–1014.
20. Blanchard,S.C., Kim,H.D., Gonzalez,R.L. Jr, Puglisi,J.D. and Chu,S.
(2004) tRNA dynamics on the ribosome during translation. Proc.
Natl. Acad. Sci. U.S.A., 101, 12893–12898.
936 Nucleic Acids Research, 2017, Vol. 45, No. 2
21. Fei,J., Kosuri,P., MacDougall,D.D. and Gonzalez,R.L. Jr. (2008)
Coupling of ribosomal L1 stalk and tRNA dynamics during
translation elongation. Mol. Cell, 30, 348–359.
22. Pan,D., Qin,H. and Cooperman,B.S. (2009) Synthesis and functional
activity of tRNAs labeled with fluorescent hydrazides in the D-loop.
RNA, 15, 346–354.
23. Wintermeyer,W. and Zachau,H.G. (1979) Fluorescent derivatives of
yeast tRNAPhe. Eur. J. Biochem., 98, 465–475.
24. Rodnina,M.V., Fricke,R. and Wintermeyer,W. (1994) Transient
conformational states of aminoacyl-tRNA during ribosome binding
catalyzed by elongation factor Tu. Biochemistry, 33, 12267–12275.
25. Kothe,U. and Rodnina,M.V. (2007) Codon reading by tRNAAla with
modified uridine in the wobble position. Mol. Cell, 25, 167–174.
26. Betteridge,T., Liu,H., Gamper,H., Kirillov,S., Cooperman,B.S. and
Hou,Y.M. (2007) Fluorescent labeling of tRNAs for dynamics
experiments. RNA, 13, 1594–1601.
27. Kaur,J., Raj,M. and Cooperman,B.S. (2011) Fluorescent labeling of
tRNA dihydrouridine residues: Mechanism and distribution. RNA,
17, 1393–1400.
28. Lee,T.H., Blanchard,S.C., Kim,H.D., Puglisi,J.D. and Chu,S. (2007)
The role of fluctuations in tRNA selection by the ribosome. Proc.
Natl. Acad. Sci. U.S.A., 104, 13661–13665.
29. Fei,J., Bronson,J.E., Hofman,J.M., Srinivas,R.L., Wiggins,C.H. and
Gonzalez,R.L. Jr (2009) Allosteric collaboration between elongation
factor G and the ribosomal L1 stalk directs tRNA movements during
translation. Proc. Natl. Acad. Sci. U.S.A., 106, 15702–15707.
30. Chen,J., Petrov,A., Johansson,M., Tsai,A., O’Leary,S.E. and
Puglisi,J.D. (2014) Dynamic pathways of -1 translational
frameshifting. Nature, 512, 328–332.
31. Uemura,S., Aitken,C.E., Korlach,J., Flusberg,B.A., Turner,S.W. and
Puglisi,J.D. (2010) Real-time tRNA transit on single translating
ribosomes at codon resolution. Nature, 464, 1012–1017.
32. Barhoom,S., Kaur,J., Cooperman,B.S., Smorodinsky,N.I.,
Smilansky,Z., Ehrlich,M. and Elroy-Stein,O. (2011) Quantitative
single cell monitoring of protein synthesis at subcellular resolution
using fluorescently labeled tRNA. Nucleic Acids Res., 39, e129.
33. Barhoom,S., Farrell,I., Shai,B., Dahary,D., Cooperman,B.S.,
Smilansky,Z., Elroy-Stein,O. and Ehrlich,M. (2013) Dicodon
monitoring of protein synthesis (DiCoMPS) reveals levels of
synthesis of a viral protein in single cells. Nucleic Acids Res., 41, e177.
34. Crawford,R., Torella,J.P., Aigrain,L., Plochowietz,A., Gryte,K.,
Uphoff,S. and Kapanidis,A.N. (2013) Long-lived intracellular
single-molecule fluorescence using electroporated molecules. Biophys.
J., 105, 2439–2450.
35. Manley,S., Gillette,J.M., Patterson,G.H., Shroff,H., Hess,H.F.,
Betzig,E. and Lippincott-Schwartz,J. (2008) High-density mapping of
single-molecule trajectories with photoactivated localization
microscopy. Nat. Methods, 5, 155–157.
36. English,B.P., Hauryliuk,V., Sanamrad,A., Tankov,S., Dekker,N.H.
and Elf,J. (2011) Single-molecule investigations of the stringent
response machinery in living bacterial cells. Proc. Natl. Acad. Sci.
U.S.A., 108, E365–E373.
37. Uphoff,S., Reyes-Lamothe,R., Garza de Leon,F., Sherratt,D.J. and
Kapanidis,A.N. (2013) Single-molecule DNA repair in live bacteria.
Proc. Natl. Acad. Sci. U.S.A., 110, 8063–8068.
38. Kaur,T., Mukherjea,D., Sheehan,K., Jajoo,S., Rybak,L.P. and
Ramkumar,V. (2011) Short interfering RNA against STAT1
attenuates cisplatin-induced ototoxicity in the rat by suppressing
inflammation. Cell Death Dis., 2, e180.
39. Plochowietz,A., El-Sagheer,A.H., Brown,T. and Kapanidis,A.N.
(2016) Stable End-Sealed DNA as Robust Nano-rulers for In Vivo
Single-Molecule Fluorescence. Chem. Sci., 7, 4418–4422.
40. Tokunaga,M., Imamoto,N. and Sakata-Sogawa,K. (2008) Highly
inclined thin illumination enables clear single-molecule imaging in
cells. Nat. Methods, 5, 159–161.
41. Young,J.W., Locke,J.C., Altinok,A., Rosenfeld,N., Bacarian,T.,
Swain,P.S., Mjolsness,E. and Elowitz,M.B. (2012) Measuring
single-cell gene expression dynamics in bacteria using fluorescence
time-lapse microscopy. Nat. Protoc., 7, 80–88.
42. Holden,S.J., Uphoff,S., Hohlbein,J., Yadin,D., Le Reste,L.,
Britton,O.J. and Kapanidis,A.N. (2010) Defining the limits of
single-molecule FRET resolution in TIRF microscopy. Biophys. J.,
99, 3102–3111.
43. Crocker,J.C. and Grier,D.G. (1996) Methods of digital video
microscopy for colloidal studies. J. Colloid Interface Sci., 179,
44. Michalet,X. and Berglund,A.J. (2012) Optimal diffusion coefficient
estimation in single-particle tracking. Phys. Rev. E Stat. Nonlin. Soft
Matter Phys., 85, 061916.
45. Sliusarenko,O., Heinritz,J., Emonet,T. and Jacobs-Wagner,C. (2011)
High-throughput, subpixel precision analysis of bacterial
morphogenesis and intracellular spatio-temporal dynamics. Mol.
Microbiol., 80, 612–627.
46. Plochowietz,A., Crawford,R. and Kapanidis,A.N. (2014)
Characterization of organic fluorophores for in vivo FRET studies
based on electroporated molecules. Phys. Chem. Chem. Phys., 16,
47. Sustarsic,M., Plochowietz,A., Aigrain,L., Yuzenkova,Y., Zenkin,N.
and Kapanidis,A. (2014) Optimized delivery of fluorescently labeled
proteins in live bacteria using electroporation. Histochem. Cell Biol.,
142, 113–124.
48. Aigrain,L., Sustarsic,M., Crawford,R., Plochowietz,A. and
Kapanidis,A.N. (2015) Internalization and observation of fluorescent
biomolecules in living microorganisms via electroporation. J. Vis.
Exp., doi:10.3791/52208.
49. Thompson,R.E., Larson,D.R. and Webb,W.W. (2002) Precise
nanometer localization analysis for individual fluorescent probes.
Biophys. J., 82, 2775–2783.
50. Dennis,P.P. and Bremer,H. (1974) Differential rate of ribosomal
protein synthesis in Escherichia coli B/r. J. Mol. Biol., 84, 407–422.
51. Parker,J. (1989) Errors and alternatives in reading the universal
genetic code. Microbiol. Rev., 53, 273–298.
52. Stracy,M., Lesterlin,C., Garza de Leon,F., Uphoff,S., Zawadzki,P.
and Kapanidis,A.N. (2015) Live-cell superresolution microscopy
reveals the organization of RNA polymerase in the bacterial
nucleoid. Proc. Natl. Acad. Sci. U.S.A., 112, E4390–E4399.
53. Werner,A. (2011) Predicting translational diffusion of evolutionary
conserved RNA structures by the nucleotide number. Nucleic Acids
Res., 39, e17.
54. Shi,H. and Moore,P.B. (2000) The crystal structure of yeast
phenylalanine tRNA at 1.93 A resolution: a classic structure revisited.
RNA, 6, 1091–1105.
55. Ando,T. and Skolnick,J. (2010) Crowding and hydrodynamic
interactions likely dominate in vivo macromolecular motion. Proc.
Natl. Acad. Sci. U.S.A., 107, 18457–18462.
56. Bilgin,N., Ehrenberg,M., Ebel,C., Zaccai,G., Sayers,Z., Koch,M.H.,
Svergun,D.I., Barberato,C., Volkov,V., Nissen,P. et al. (1998) Solution
structure of the ternary complex between aminoacyl-tRNA,
elongation factor Tu, and guanosine triphosphate. Biochemistry, 37,
57. Dunkle,J.A., Xiong,L., Mankin,A.S. and Cate,J.H. (2010) Structures
of the Escherichia coli ribosome with antibiotics bound near the
peptidyl transferase center explain spectra of drug action. Proc. Natl.
Acad. Sci. U.S.A., 107, 17152–17157.
58. Aubin,J.E. (1979) Autofluorescence of viable cultured mammalian
cells. J. Histochem. Cytochem., 27, 36–43.
59. Tynan,C.J., Clarke,D.T., Coles,B.C., Rolfe,D.J.,
Martin-Fernandez,M.L. and Webb,S.E. (2012) Multicolour single
molecule imaging in cells with near infra-red dyes. PLoS One, 7,
60. Potts,R.O., Ford,N.C. Jr and Fournier,M.J. (1981) Changes in the
solution structure of yeast phenylalanine transfer ribonucleic acid
associated with aminoacylation and magnesium binding.
Biochemistry, 20, 1653–1659.
61. Antosiewicz,J. and Porschke,D. (1989) Effect of aminoacylation on
tRNA conformation. Eur. Biophys. J., 17, 233–235.
62. Nenninger,A., Mastroianni,G. and Mullineaux,C.W. (2010) Size
dependence of protein diffusion in the cytoplasm of Escherichia coli.
J. Bacteriol., 192, 4535–4540.
63. Elf,J., Li,G.W. and Xie,X.S. (2007) Probing transcription factor
dynamics at the single-molecule level in a living cell. Science, 316,
64. Gouy,M. and Grantham,R. (1980) Polypeptide elongation and
tRNA cycling in Escherichia coli: a dynamic approach. FEBS Lett.,
115, 151–155.
Nucleic Acids Research, 2017, Vol. 45, No. 2 937
65. Negrutskii,B.S. and Deutscher,M.P. (1991) Channeling of
aminoacyl-tRNA for protein synthesis in vivo. Proc. Natl. Acad. Sci.
U.S.A., 88, 4991–4995.
66. Mirande,M. (2010) Processivity of translation in the eukaryote cell:
role of aminoacyl-tRNA synthetases. FEBS Lett., 584, 443–447.
67. Defeu Soufo,H.J., Reimold,C., Linne,U., Knust,T., Gescher,J. and
Graumann,P.L. (2010) Bacterial translation elongation factor EF-Tu
interacts and colocalizes with actin-like MreB protein. Proc. Natl.
Acad. Sci. U.S.A., 107, 3163–3168.
68. Klumpp,S., Scott,M., Pedersen,S. and Hwa,T. (2013) Molecular
crowding limits translation and cell growth. Proc. Natl. Acad. Sci.
U.S.A., 110, 16754–16759.
69. Dong,H., Nilsson,L. and Kurland,C.G. (1996) Co-variation of tRNA
abundance and codon usage in Escherichia coli at different growth
rates. J. Mol. Biol., 260, 649–663.
70. Mackie,G.A. (2013) RNase E: at the interface of bacterial RNA
processing and decay. Nat. Rev. Microbiol., 11, 45–57.
71. Wang,W., Li,G.W., Chen,C., Xie,X.S. and Zhuang,X. (2011)
Chromosome organization by a nucleoid-associated protein in live
bacteria. Science, 333, 1445–1449.
72. Siibak,T., Peril,L., Dönhöfer,A., Tats,A., Remm,M., Wilson,D.N.,
Tenson,T. and Remme,J. (2011). Antibiotic-induced ribosomal
assembly defects result from changes in the synthesis of ribosomal
proteins. Mol. Microbiol., 80, 54–67.